AN EFFICIENT AND STABLE ROUTING ALGORITHM IN MOBILE AD HOC NETWORK

International Journal of Computer Networks & Communications (IJCNC) Vol.13, No.4, July 2021
DOI: 10.5121/ijcnc.2021.13402 21
AN EFFICIENT AND STABLE ROUTING
ALGORITHM IN MOBILE AD HOC NETWORK
Priyanka Pandey and Raghuraj Singh
Department of Computer Science and Engineering,
Harcourt Butler Technical University, Kanpur, India.
ABSTRACT
Mobile Ad hoc Network (MANET) is mainly designed to set up communication among devices in
infrastructure-less wireless communication network. Routing in this kind of communication network is
highly affected by its restricted characteristics such as frequent topological changes and limited battery
power. Several research works have been carried out to improve routing performance in MANET.
However, the overall performance enhancement in terms of packet delivery, delay and control message
overhead is still not come into the wrapping up. In order to overcome the addressed issues, an Efficient
and Stable-AODV (EFST-AODV) routing scheme has been proposed which is an improvement over AODV
to establish a better quality route between source and destination. In this method, we have modified the
route request and route reply phase. During the route request phase, cost metric of a route is calculated on
the basis of parameters such as residual energy, delay and distance. In a route reply phase, average
residual energy and average delay of overall path is calculated and the data forwarding decision is taken
at the source node accordingly. Simulation outcomes reveal that the proposed approach gives better
results in terms of packet delivery ratio, delay, throughput, normalized routing load and control message
overhead as compared to AODV.
KEYWORDS
Mobile Ad hoc Network, Energy, AODV, Routing, Random Waypoint Model.
1. INTRODUCTION
In the field of wireless communication technology, Mobile Ad hoc Networks (MANET) [1, 2]
became one of the most prevalent topics among the research community. In MANET, mobile
nodes move arbitrarily and communicate among themselves in wireless mode. MANET has many
issues such as real-time communication, limited bandwidth, resource constraints and control
message overhead. Owing to these issues, routing in this kind of networks is very challenging
task. Over the past few years, many routing protocols [3] has been designed and broadly
classified as reactive and proactive. These routing protocols specify the strategy through which
two devices communicate with each other.
In the proactive routing approach, every node has routing information about the available routes
and routing table is updated periodically. DSDV [4] and OLSR [5] are some examples of
proactive routing protocols. However, these protocols are not efficient for the large and dynamic
network.
On the other hand, reactive to routing protocols such as AODV [6] and DSR [7] use local
information for the route establishment. Nodes do not maintain routing information periodically.
The route discovery process is initiated only when a source needs to transfer data packets to a

22
goal node. AODV is one the most widely used routing scheme in the reactive category as it
performs routing operation more efficiently in dynamic environment where network topology
changes frequently. If a path is not used till the expiration time, the information from the routing
table automatically gets deleted.
Stable routing helps in selection of long lasting route which reduces chances of occurrences of
route failure. An efficient routing protocol focuses on improving the routing performance.
Consideration of stability along with efficiency gives improved results in terms of packet delivery
ratio, normalized routing load, message overhead, throughput and delay. Parameters such as
residual energy of a node, distance and node mobility have great impact on link stability [8]. For
example, if the distance between adjacent nodes is longer then it may result in packet loss.
Furthermore, weak signal strength may also cause packet drop. Nodes are dependent on battery
power and consume energy during processing, receiving and transmitting packets [9]. Thus, the
residual energy of a node should also be considered during route establishment process. The
stability- based routing protocol should be designed in such a way that it selects the route with a
longer lifetime and be more adaptive to the dynamic topology of MANET.
Therefore, in this paper, we have modified the existing AODV scheme in order to provide more
stable and efficient route to the network for smooth data transmission process. Parameters such as
received signal power, distance, delay and residual energy are used for the computation of route
stability.
The paper is organized as follows: Section 2 explains related work done pertaining to the
enhancement of AODV routing performance. In Section 3 detailed description of EFST-AODV
has been presented. Section 4 presents the result and analysis along with description of simulation
parameters and network scenarios that are used to validate the proposed approach. Finally, in
Section 5 conclusion is drawn.
2. RELATED WORK
Enhancement of routing performance has been comprehensively researched in past few years and
paradigms that are said to possess the ability to provide efficient and stable communication
between sender and receiver have been designed. In this section we discuss about some related
works done in the direction towards improving routing performance in MANET.
Received signal power is considered as one of the important factors in route selection criteria. It
depicts the link quality between two nodes. Considering this parameter [10] proposed a technique
in which node determines received signal power in order to estimate signal quality. If the
estimated value of received power is below threshold, the node increases link failure factor (LFF)
metric by one. After getting route reply message from all paths, the source node selects the final
path with minimum LFF. Furthermore, during data transmission process energy level of the hops
are continuously monitored. Another proposed Advanced AODV Link Break Prediction
(AODVLBP) [11] utilizes the signal strength parameter to examine the route stability consisting
of multiple links. The scheme predicts the time of link failure using Newton Divided difference
method which is applied on latest three received signal strength values. In order to transmit data
smoothly and efficiently authors proposed Intelligent Routing AODV (IRAODV) [12] that
focuses on the improvement of battery power. In this scheme distance between two nodes is
determined on the basis of received signal power. However, performance in terms of throughput
and delay has not shown much improvement. Authors [13] suggested a technique by considering
difference between values of last and current hop received signal power. If the difference is found
below a threshold, the scheme applies local the route repairing technique.

23
During route construction process quality of link should also be examined. For example [14]
Malwe et. al. proposed two link availability prediction scheme i.e. zone based and segment-
based. In zone- based technique transmission range is divided into three zones i.e. inner, middle
and outer. Nodes in the middle zone are only allowed to transmit control packets. Another
proposed approach is an enhancement over zone based technique in which nodes lying in the
external zone are also allowed for control packets transmission. Next hop is selected on the basis
of current available position of the neighbour. In order to improve routing performance, tracking
concept [15] is applied to predict node mobility. The scheme initially selects the route as per
conventional AODV. During data transmission process the tracking mechanism is applied to
approximate node’s coordinate and correlation is established to find the node’s next position.
However, as the network load increases, energy consumption also increases.
Energy consumption is one of the important problems in MANET as nodes have limited battery
power. In order to reduce energy consumption [16] Senthil R. et. al. proposed a technique for
enhancement of AODV based on energy level of a node. The approach selects the path by
investigating energy level of a node with more throughputs and less delay. In another notable
work, Dhandapani et. al. proposed [17] an Energy Efficient Node Rank Based Routing (EENRR)
in order to improve networks lifetime and packet delivery performance. The technique is an
improvement over DSR which has almost similar working as AODV. Before forwarding route
request packet, rank of the intermediate node is computed. Afterward, the energy level is
compared with threshold. The node forwards the route request packet further only if the residual
energy is higher than a threshold.
In order to take good routing decisions fuzzy approach is applied by considering energy
parameters along with other criteria factors such as node mobility, hop count and distance to
improve routing efficiency. For example in [18] used a fuzzy approach by considering residual
energy, distance and node mobility in route selection criteria. However, the technique has a
higher delay. In [19] Fuzzy AODV technique computes the trust level of a node to select an
optimal route by considering parameters such as node energy, mobility and hop count. These
parameters are fed into the fuzzy inference system and nodes with the highest trust values are
chosen to establish final route towards the destination.
From the analysis, it can be concluded that most of the aforementioned techniques consider
received signal power and energy parameters during the route discovery process but do not
achieve better performance in every aspect. For example, the mechanisms applied in [10] [16]
and [18] have a higher delays. While in [14] control message overhead is more. In [13]
simulation has not been performed to analyze the results. In [19] and [11] performance in terms
of throughput and packet delivery ratio has not shown significant improvement. Considering
these drawbacks, in this paper, our technique provides more stable and efficient route by
incorporating some major parameters in order to investigate the quality of nodes and links during
the route discovery cycle. The performance of the work has shown significant improvement in
terms of delay, packet delivery, normalized routing load, control overhead and throughput.
3. PROPOSED WORK
The main aim of the proposed EFST-AODV is to enhance the routing performance by providing
a stable and efficient route. Link failure, node mobility and limited energy are major issues in
MANET routing. Therefore, in this work, we have considered delay, distance and received signal
power parameters in order to estimate link quality and residual energy for investigating node
status so that the final chosen path can perform data transmission process more efficiently.

24
3.1. Parameters Considered
In EFST-AODV, following parameters are taken into account during route establishment
process.
3.1.1. Residual Energy
Each node acts differently as per the amount of remaining energy. The proposed technique
considers the energy level of a node during route selection process. Every mobile node consumes
energy mainly in transmitting, receiving and idle mode [20]. Therefore, the total energy
consumption of a particular node 𝑁𝑖 till the current time can be calculated using the following
equation 1.
𝐸𝑐𝑜𝑛 𝑖 = 𝐸𝑟𝑖 + 𝐸𝑖𝑑𝑖 + 𝐸𝑡𝑟 𝑖 (1)
Where 𝐸𝑟𝑖 is total energy consumed in sending mode.
𝐸𝑖𝑑𝑖 is total energy consume in idle mode
𝐸𝑡𝑟𝑖 is total energy consumed in receiving mode.
Further the consumed energy 𝐸𝑐𝑜𝑛 is subtracted from initial energy 𝐸𝑖𝑛 to get node’s residual
energy 𝐸𝑟𝑒 as in equation 2
𝐸𝑟𝑒 = 𝐸𝑖𝑛 − 𝐸𝑐𝑜𝑛 (2)
3.1.2. Distance
Distance parameter is used in order to determine link connectivity status between adjacent nodes.
In this paper, distance between two nodes, let’s 𝑁𝑖 and 𝑁𝑗 at time t is determined using Euclidian
distance formula as in equation 3.
𝐷𝑖𝑗 = √(𝑥𝑗 − 𝑥𝑖)2 + (𝑦𝑗 − 𝑦𝑖)2 (3)
Where (𝑥𝑖, 𝑦𝑖) and (𝑥𝑗, 𝑦𝑗) are the current position of node 𝑁𝑖 and 𝑁𝑗 respectively.
If the distance is less than threshold 𝜃, the nodes are said to be neighbours. Threshold θ is
determined using equation 4.
𝜃 = 𝐶𝑟 ∗ 𝑎 (4)
Where value of 𝑎 is .96 and Cr is the communication range= 250 m.
3.1.3. Delay (Del)
Delay is the difference between receiving time and sending time. It also shows congestion level
between adjacent nodes. If the delay between nodes is higher, it leads to the delay in overall
routing process.

25
3.1.4. Received Signal Power
Received signal strength measures signal quality. The value comes from the physical layer to
choose stable and efficient links by monitoring link quality [21]. The two ray propagation model
[22] is used to estimate received signal power. In the proposed approach, this parameter is one of
the main factors in modifying the routing strategy.
Following equation 5 is used to estimate received signal power.
𝑃𝑟 =
𝐺𝑡𝑟𝑎𝑛𝑠𝑚∗𝐺𝑟𝑒𝑐𝑒𝑖𝑣∗𝑃𝑡𝑟𝑎𝑛𝑠𝑚∗𝐻𝑡𝑟𝑎𝑛𝑠𝑚
2∗𝐻𝑟𝑒𝑐𝑒𝑖𝑣
2
𝑑4 𝐿
(5)
Where 𝑃𝑟 is received signal power, 𝐺𝑡𝑟𝑎𝑛𝑠𝑚 𝑎𝑛𝑑 𝐺𝑟𝑒𝑐𝑒𝑖𝑣 are transmitted and received antenna
gain respectively, 𝐻𝑡𝑟𝑎𝑛𝑠𝑚 𝑎𝑛𝑑 𝐻𝑟𝑒𝑐𝑒𝑖𝑣 are heights of antennas, d is distance and L is path loss.
3.2. Route Establishment Process
The proposed scheme establishes the route by considering more criteria factors. The route
establishment procedure is mainly performed in two phases i.e route request and route reply. In
the route request stage, the originator node initiates route-finding operation by broadcasting route
request (RREQ) packets. In route reply phase the destination node sends a route reply (RREP)
packet to the originator that has complete routing information.
3.2.1. Route Request Phase
Whenever the source node has to transmit the packet to a goal node and it has no information
about the path. The node broadcasts the RREQ packet towards its neighbor. However, the
conventional AODV algorithm does not consider neighbor connectivity and energy status that
may lead to route failure. In order to enhance route lifetime, the RREQ packet is modified
slightly by adding fields such as packet sending time, node’s position and one more field i.e. cost
metric field which represents the stability level of a path from source to destination. The modified
packet format is shown in Figure 1.
Figure 1: RREQ Packet Format (SA-Address of Source, DA - Destination Address, SSN : Source Sequence
Number, DSN - Destination Sequence Number, X pos - position of X, Y pos -position of Y , CMFR- Cost
Metric of Forward Route, HC - Hop Count)
Value of Cost Metric is initialized to 0. Nodes receiving packet calculates distance and received
signal power as per equation 3 and 5 respectively. In order to examine link quality these
parameters are compared with respect to their thresholds as shown in equation 6.
{
(𝐷𝑖𝑗 > 𝐷𝑡ℎ𝑟) 𝑎𝑛𝑑 (𝑆𝑆 > 𝑆𝑆𝑡ℎ𝑟) 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒 𝐶𝑀𝐹𝑅
𝑂𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 𝐷𝑖𝑠𝑐𝑎𝑟𝑑 𝑃𝑎𝑐𝑘𝑒𝑡
(6)
Where 𝐷𝑖𝑗 is the distance between two nodes, 𝐷𝑡ℎ𝑟 = 𝜃, 𝑆𝑆 = 𝑃𝑟 and 𝑆𝑆𝑡ℎ𝑟 is signal threshold.
SA DA SSN DSN X pos Y pos CMFR HC

26
If a receiving node satisfies the packet acceptance criteria, it further calculates CMFR
(Cost Metric of Forward Route) using equation 7 and the value is appended in RREQ’s
CMFR field which is further broadcasted and processed until it reaches the goal node.
𝐶𝑀𝐹𝑅 = (𝑘1 ∗
1
𝐷𝑒𝑙
) + (𝑘2 ∗
1
𝑫𝒊𝒋
) + (𝑘3 ∗ 𝐸𝑟𝑒) (7)
Once the destination node receives first RREQ packet, it immediately replies to the
source node through route reply packet. Later, when another RREQ packet with better
cost metric arrives, that packet is sent towards the originator for further processing.
The whole Route Request Phase is represented through following pseudo code.
Let P be the RREQ packet
CMFR be the cost metric of forward route whose value representing the route stability.
𝑪𝑴𝑭𝑹𝒑𝒓𝒆𝒗 be the 𝑪𝑴𝑭𝑹 value of previous packet
𝑫𝒊𝒋 𝐚𝐧𝐝 𝑫𝒕𝒉𝒓 be the distance and distance threshold respectively.
𝑺𝑺 𝐚𝐧𝐝 𝑺𝑺𝒕𝒉𝒓 be the received signal power and its threshold respectively.
Route Reply Phase
Step1: Estimate 𝑫𝒊𝒋 and 𝑺𝑺 using equation and respectively.
Step 2: If (𝑫𝒊𝒋 > 𝑫𝒕𝒉𝒓) 𝒂𝒏𝒅 (𝑺𝑺 > 𝑺𝑺𝒕𝒉𝒓)
Evaluate 𝑪𝑴𝑭𝑹
Else
Discard P
Step 3: Evaluate 𝑪𝑴𝑭𝑹 using equation
Step 4: If first packet is received
Store 𝑪𝑴𝑭𝑹 in a table and send reply to S
Else
If (𝑪𝑴𝑭𝑹 > 𝑪𝑴𝑭𝑹𝒑𝒓𝒆𝒗)
Send RREP to S
Else
Discard P
Figure 2: Pseudo code for Route Request phase
3.2.2. Route Reply Phase
During route reply phase each intermediate node receiving RREP estimates residual
energy and current delay between sender and receiver and adds this information into the
RREP field before forwarding the packet to the next hop towards source node. The RREP
packet format is modified as shown below in Figure 3:
Figure 3: RREP Packet Format (SA- Address of source, DA - Destination Address, SSN : Source Sequence
Number, DSN - Destination Sequence Number, , HC - Hop Count, AVG_DEL - Average Delay,
AVG_ENG- Average Residual Energy)
SA DA SSN DSN HC AVG_DEL AVG_ENG

27
Once the source node receives the packet, it first estimates Cost Metric of Backward Route
(CMBR) using equation 8 as follows
𝐶𝑀𝐵𝑅 = 𝐴𝑉𝐺_𝐸𝑁𝐺 ∗ 𝛼 + 𝐴𝑉𝐺_𝐷𝐸𝐿 ∗ (1 − 𝛼) (8)
Where AVG_ENG is average energy and 𝐴𝑉𝐺_𝐷𝐸𝐿 is average delay and 𝛼 is weighting factor.
At a later stage, if before the expiration time, the second packet RREP arrives at the source node,
CMBR is estimated by the receiving node. If the value is higher than the previous received
packet, rest data packets are transmitted through new route. Otherwise, the received packet is
simply discarded and the flow continues with the previous route. The working flow of the route
reply packet is shown in Figure 4.
Figure 4: Working Flow Route Reply Phase
Start
Node estimates Average delay and
average residual energy and add in
RREP
Forward RREP Packet
If Source
Node
If First
Packet
Estimate Cost
of the Route If packet
with better
cost arrives
Send the data
through new route
End
Send data through
first route
Yes
No
Yes
No
No
Yes

28
4. SIMULATION, RESULTS AND ANALYSIS:
4.1. Simulation Environment
Network Simulator 2.35 [23] is used to evaluate performance of proposed EFST-AODV scheme
under Ubuntu operating system. It is a discrete event object -oriented simulator. The simulation
script is written using OTCL programming language. In the scheme, Random Waypoint [24]
mobility model is used for node movement in which the node waits for a certain pause time
before it moves to a new location and constant bit rate (CBR) is used as a traffic source. Total
60 nodes are arbitrarily deployed in the simulation region of 900 m x 900 m. The simulation
parameters used in this approach are shown in Table 1.
Table 1 : Main Simulation Parameters
4.2. Performance Metrics
In order to evaluate the performance of a proposed approach, the following performance metrics
have been considered.
Packet delivery ratio: It is the proportion of total data packets received by the goal node to the
total packets generated at the source.
Throughput (in kbps): It is the number of bytes received by target node per unit of time.
Normalized routing load: It depicts the number of control packets required in sending a single
data packet from source to goal node.
Control Overheads (packets): It is the total amount of control messages generated by the
routing algorithm during entire simulation.
End-to-end delay (seconds): The average time taken by routing algorithm to transfer data
packets from source to destination is known as end-to-end delay.
A Total number of received packets: It indicates total amount of data packets received by the
destination during the entire simulation.
Simulator NS 2.35
Simulation Area 900x900
Traffic CBR
Mobility Model Random Way Point
Antenna Omni Directional
Propagation Model Two Ray Ground
Pause Time 2, 4, 6, 8, and 10 seconds
Simulation Time 100, 200, 300, 400 and 500
seconds
Number of Nodes 60
Packet Rate 5 packet/sec
Node Speed 30 m/s
Routing Protocol EFST-AODV and AODV

29
4.3. Results and Analysis
In this section, obtained results are represented in the graph and analysis of the results has been
discussed in depth. Figure 5 and 6 shows the snapshots of network animator used for
visualization purpose. Figure 5 represents the initial deployment of nodes. Figure 6 is the
snapshot taken during communication. The Encircled node represents communication range and
along with it packet dropping can also be seen in the figure.
Figure 5: Snapshot of Network Animator during node deployment
Figure 6: Snapshot of Network Animator showing communication

30
4.3.1. Impact of Varying Pause Time:
Simulation has been conducted by varying pause time to 2, 4, 6, 8 and 10 seconds in each case
with maintaining node speed to 30m/s.
Figure 7: Packet Delivery Ratio Vs Pause Time
Figure 7 illustrates the packet delivery ratio (PDR) performance of proposed and conventional
routing. Based on results the PDR of the proposed approach is 5.41% higher than conventional.
This is due to the capability of EFST-AODV in coping with the link quality by examining
distance and received signal power at receiving RREQ packets.
Figure 8: Throughput (in kbps) Vs Pause Time

31
Figure 9: Number of Received Packets Vs Pause Time
Figure 8 shows aggregated throughput performance. It can be seen that EFST-AODV
outperforms AODV. The proposed approach has 5.86 % higher throughput. Similarly, a number
of received packets is 5.67 % more than the standard AODV as shown in Figure 9. This is
because, the scheme estimates the cost in terms of distance, delay and residual energy during
route request and route reply phase which leads to an increase in rote stability and thus more
packets are delivered to the destination.
Figure 10: End to End Delay Vs Pause Time
Figure 10 depicts delay performance by varying pause time. The proposed EFST-AODV
approach has 28.57% less delay in comparison with AODV. This is because, during the route
establishment process, delay parameter is taken into account by the proposed scheme. Moreover,
the scheme provides a stable route resulting in a reduction of re-route discovery process.

32
Figure 11: Control Message Overhead Vs. Pause Time
Figure 12: Normalized Routing Load Vs Pause Time
Figure 11 and Figure 12 show control overhead and normalized routing load performance
respectively by varying pause time. In EFST-AODV the control overhead is reduced by 8.24%
when compared with standard AODV. Similarly, normalized routing load is also reduced to
11.58% as compared with conventional ones. This is because during the route request phase
inefficient nodes are filtered and thus control message load on each node in finding data packets
is reduced.

33
4.3.2. Impact of Varying Simulation Time
In the second experiment, the time of simulation was varied to 100, 200, 300, 400 and 500
seconds.
Figure 13: Packet Delivery Ratio Vs Simulation Time
Figure 13 shows the packet delivery ratio (PDR) performance of EFST-AODV and AODV. From
the graph, it can be concluded that PDR of the suggested technique is 5.36% higher than the
standard AODV. This is because; the proposed work focuses on making the route stable and
efficient which leads to less packet drop.
Figure 14: Throughput Vs Simulation Time
Figure 14 depicts throughput performance by varying simulation time. From the figure, it can be
concluded that the proposed approach achieves 7.16 % better throughput. This is due to the
reason that the proposed approach constructs a stable route by focusing on signal power, delay
and distance parameters. Consideration of these help in examining link quality more accurately

34
during route establishment thus the packets are forwarded through a more efficient link leading to
increase in throughput performance.
Figure 15: Number of Received Packets Vs Simulation Time
Figure 15 depicts performance of the proposed approach in terms of total received packets. From
the graph, it can be concluded that the number of received packets are increasing with an increase
in simulation time. The proposed approach has 4.90% higher performance in terms of received
packets as compared to AODV. This shows that the approach constructs a more stable and long-
lasting route.
Figure 16: End to End Delay Vs. Simulation Time
Figure 16 illustrates the delay performance of the EFST-AODV and AODV. There is a drastic
reduction in delay using EFST-AODV algorithm. The proposed scheme has 43.75% less delay as
compared to the standard algorithm. This is due to the reduction in occurrence of route failure as
the breakage causes initiation of a new route discovery process which ultimately increases overall
routing delay in delivering data packets.

35
Figure 17: Normalized Routing Load Vs. Simulation Time
Figure 18: Control Overhead Vs Simulation Time
The graph in Figure 17 shows the performance of routing protocols in terms of routing load. The
load in the case of the suggested EFST-AODV is 9.66% less as compared to AODV. Similarly,
the control message overhead is also reduced by 8.46 % as compared to AODV as shown in
Figure 18. This is because; the proposed approach discards those control packets that are coming
from either an efficient node or a link.
5. CONCLUSION
In this paper, an efficient and stable routing algorithm has been proposed to enhance the
performance of the AODV routing protocol. The scheme is divided into two phases route request
and route reply. During the route request phase, link quality is monitored before broadcasting the
RREQ packet. Furthermore, the cost metric for a forward route is estimated and added in RREQ
field. Similarly, during route reply phase, delay and residual energy parameters are taken into
account. By simulation study, it can be concluded that the proposed approach performs efficiently
in different network scenarios i.e. by varying pause and simulation time. The technique achieves

36
higher throughput, has better packet delivery ratio, less control message overhead, normalized
load and delay as compared to AODV. In the future, an energy conservation technique can be
incorporated which may further increase network lifetime. Moreover, security features can also
be added to improve routing performance.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
REFERENCES
[1] Loo, J., Mauri, J. L., & Ortiz, J. H. (Eds.). (2016). Mobile ad hoc networks: current status and future
trends. CRC Press.
[2] Basagni, S., Conti, M., Giordano, S., & Stojmenovic, I. (Eds.). (2004). Mobile ad hoc networking.
John Wiley & Sons.
[3] Gulati, M. K., & Kumar, K. (2014). Performance comparison of mobile Ad Hoc network routing
protocols. International Journal of Computer Networks & Communications, 6(2), 127.
[4] He, G. (2002). Destination-sequenced distance vector (DSDV) protocol. Networking Laboratory,
Helsinki University of Technology, 135.
[5] Clausen, T., Jacquet, P., Adjih, C., Laouiti, A., Minet, P., Muhlethaler, P., ... & Viennot, L. (2003).
Optimized link state routing protocol (OLSR).
[6] Perkins, C., Belding-Royer, E., & Das, S. (2003). Ad hoc on-demand distance vector (AODV)
routing.
[7] Johnson, D. B., Maltz, D. A., & Broch, J. (2001). DSR: The dynamic source routing protocol for
multi-hop wireless ad hoc networks. Ad hoc networking, 5(1), 139-172.
[8] Er-rouidi, M., Moudni, H., Faouzi, H., Mouncif, H., & Merbouha, A. (2017, May). A fuzzy-based
routing strategy to improve route stability in manet based on aodv. In International Conference on
Networked Systems (pp. 40-48). Springer, Cham.
[9] Ray, N. K., & Turuk, A. K. (2009). A survey on energy-aware mac for wireless network.
[10] Dhakad, C., & Bisen, A. S. (2016, March). Efficient route selection by using link failure factor in
MANET. In 2016 International Conference on Electrical, Electronics, and Optimization Techniques
(ICEEOT) (pp. 3740-3743). IEEE.
[11] Mohammed, Y., Abdelhaq, M., & Alsaqour, R. PREDICTION ALGORITHM FOR MOBILE AD
HOC NETWORK CONNECTION BREAKS. International Journal of Computer Networks &
Communications (IJCNC) Vol.12, No.6, November 2020.
[12] Anand, M., & Sasikala, T. (2019). Efficient energy optimization in mobile ad hoc network
(MANET) using better-quality AODV protocol. Cluster Computing, 22(5), 12681-12687.
[13 ] Charu Gupta & Pankaj Sharma (2014). An Approach to Link Failure in MANET. International
Journal of Computer Science and Network Security, VOL.14 No.11.
[14] Malwe, S. R., Taneja, N., & Biswas, G. P. (2017). Enhancement of DSR and AODV protocols using
link availability prediction. Wireless Personal Communications, 97(3), 4451-4466.
[15] Pandit, K., & Singh, M. (2017). A Route Stability Routing Protocol for Improving the Performance of
AODV Routing. International Journal of Research and Scientific Innovation (IJRSI) | Volume IV,
Issue VIIS, July 2017.
[16] Kumar, R. S., & Manikandan, P. (2018). Enhancement of AODV protocol based on energy level in
MANETs. International journal of pure and applied mathematics, 118(7), 425-430.
[17] Dhandapani, Kothandaraman & C, Chellappan. (2019). Energy Efficient Node Rank-Based Routing
Algorithm in Mobile Ad-Hoc Networks. International journal of Computer Networks &
Communications. 11. 45-61. 10.5121/ijcnc.2019.11103.
[18] Er-rouidi, M., Moudni, H., Faouzi, H., Mouncif, H., & Merbouha, A. (2017, May). A fuzzy-based
routing strategy to improve route stability in manet based on aodv. In International Conference on
Networked Systems (pp. 40-48). Springer, Cham.
[19 ] Abbas, N., Ilkan, M., & Ozen, E. (2016). Fuzzy approach to improving route stability of the AODV
routing protocol. EURASIP J. Wireless Comm. and Networking, 2016, 94.
[20] Gu, C., & Zhu, Q. (2014). An energy-aware routing protocol for mobile ad hoc networks based on
route energy comprehensive index. Wireless personal communications, 79(2), 1557-1570.

37
[21] Amel, B., & Zoulikha, M. M. (2014). The effects of physical layer on the routing wireless
protocol. Wireless personal communications, 77(1), 749-765.
[22] Stepanov, I., Herrscher, D., & Rothermel, K. (2005, September). On the impact of radio propagation
models on MANET simulation results. In Proceedings of 7th International Conference on Mobile and
Wireless Communications Networks (MWCN 2005), Marrakech, Morocco.
[23] Issariyakul, Teerawat & Hossain, Ekram. (2012). Introduction to Network Simulator 2 (NS2).
10.1007/978-1-4614-1406-3_2.
[24] Bettstetter, C., Resta, G., & Santi, P. (2003). The node distribution of the random waypoint mobility
model for wireless ad hoc networks. IEEE Transactions on mobile computing, 2(3), 257-269.
AUTHORS
Priyanka Pandey received her B. Tech. and M. Tech. degree in Computer Science and
Engineering from Dr. A.P.J. Abdul Kalam Technical University, Lucknow. She is
currently pursuing her Ph.D. in the Computer Science and Engineering Department of
Harcourt Butler Technical University, Kanpur, India. Her research area is Mobile Ad hoc
Networks.
Raghuraj Singh, received his Ph.D. degree from U. P. Technical University, Lucknow,
India in 2006 and M. S. from BITS, Pilani, India in 1997. He has received B. Tech. in
Computer Science and Engineering from Harcourt Butler Technological Institute, Kanpur,
India in 1990. He is currently working as Professor at Computer Science and Engineering,
Harcourt Butler Technical University, Kanpur, India. His research interest includes
Software Engineering, Human-Computer Interaction and Mobile Ad hoc Networks. Dr.
Singh has published about 140 research papers in International Journals and Conferences including IEEE,
Springer etc.

AN EFFICIENT AND STABLE ROUTING ALGORITHM IN MOBILE AD HOC NETWORK

More Related Content

What's hot (17)

Similar to AN EFFICIENT AND STABLE ROUTING ALGORITHM IN MOBILE AD HOC NETWORK (20)

More from IJCNCJournal (20)

Recently uploaded (20)

AN EFFICIENT AND STABLE ROUTING ALGORITHM IN MOBILE AD HOC NETWORK